请问HN：你是如何为你的平台构建一个“信任层”的？
我正在尝试了解不同团队在扩展过程中如何处理平台完整性和用户信任的问题。
对于那些运营消费应用、市场平台、金融科技产品或任何用户活动显著的平台的团队：
你们目前是如何构建“信任层”的？
具体来说：
- 你们如何检测虚假用户、机器人、设备农场或自动注册？
- 你们依赖哪些早期信号来识别可疑行为？
- 你们是否收集任何行为、设备或网络层的数据来做出信任决策？
- 你们的技术栈中有多少是自家开发的，多少是第三方的？
- 在扩展过程中，哪些做法有效，哪些做法无效？
- 如果今天重新构建你们的信任/欺诈处理流程，你们会做哪些改变？
我希望从不同领域的真实经验中学习，任何你们可以分享的内容（架构、失败、教训、希望存在的工具）都将非常有帮助。
谢谢！
查看原文
I'm trying to understand how different teams handle platform integrity and user trust as they scale.<p>For those running consumer apps, marketplaces, fintech products, or any platform with significant user activity:
How do you currently build your “trust layer”?<p>Specifically:
-> How do you detect fake users, bots, device farms, or automated signups?
-> What early signals do you rely on to identify suspicious behavior?
-> Do you collect any behavioral, device, or network-level data to make trust decisions?
-> How much of your stack is home-grown vs third-party?
-> What worked well and what didn’t as you scaled?
-> If you rebuilt your trust/fraud pipeline today, what would you change?<p>I’m trying to learn from real experiences across different industries anything you can share (architecture, failures, lessons, tools you wish existed) would be super helpful.
Thanks!